Abstract
In this chapter, a self-organized and scalable multiple-camera tracking system that tracks human across the cameras with nonoverlapping views is introduced. Given the GPS locations of uncalibrated cameras, the system automatically detects the existence of camera link relationships within the camera network based on the routing information provided by Google Maps. The connected zones in any pair of directly-connected cameras are identified based on the feature matching between the camera’s view and Google Street View. To overcome the adverse issues of nonoverlapping field of views among cameras, we propose an unsupervised learning scheme to build the camera link model, including transition time distribution, brightness transfer function, region mapping matrix, region matching weights, and feature fusion weights. Our unsupervised learning scheme tolerates well the presence of outliers in the training data and the learned camera link model can be continuously updated even after the tracking is started. The systematic integration of multiple features enables us to perform an effective re-identification across cameras. The pairwise learning and tracking manner also enhances the scalability of the system. Thanks to the unsupervised pairwise learning and tracking in our system, the camera network is self-organized, and our proposed system is able to be scale up efficiently when more cameras are added into the network. Thanks to the unsupervised pairwise learning and tracking in our system, the camera network is self-organized, and our proposed system is able to scale up efficiently when more cameras are added into the network.
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Lee, KH., Chu, CT., Lee, Y., Fang, Z., Hwang, JN. (2014). Consistent Human Tracking Over Self-organized and Scalable Multiple-camera Networks. In: Bobda, C., Velipasalar, S. (eds) Distributed Embedded Smart Cameras. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7705-1_9
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DOI: https://doi.org/10.1007/978-1-4614-7705-1_9
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